4688results about "Tunnels" patented technology

The invention relates to the technical field of tunnel engineering intelligent construction, and discloses a hard rock TBM shield tunneling machine auxiliary tunneling parameter dynamic adaptation regulation and control system, which comprises a high-precision multi-source sensing network, a high-precision multi-source sensing network, a high-precision multi-source sensing network, a high-precision multi-source sensing network, a high-precision multi-source sensing network, a high-precision multi-source sensing network and a high-precision multi-source sensing network, the TBM-geological environment digital twin predicts the tunneling short-term trend based on the high-fidelity physical simulation and data assimilation technology; and the multi-modal deep learning collaborative decision-making module deeply fuses real-time and prediction data and generates an optimal parameter solution set through a network trade-off tunneling multi-conflict target based on Pareto optimization. And the system executes a decision and forms closed-loop feedback through a parameter dynamic adaptation and adaptive learning module, and continuously optimizes a self model and a knowledge base. According to the method, passive response of TBM tunneling is converted into active pre-judgment, the decision accuracy, the construction safety and the comprehensive tunneling efficiency under the complex working condition are remarkably improved, and the method has the sustainable evolution capacity.

The invention discloses a multi-source collaborative advanced geological exploration method based on TBM construction, and belongs to the technical field of tunnel engineering geological exploration. A three-dimensional geological model is constructed through multi-scale collaborative operation of directional long exploration drilling, branch short exploration drilling and in-hole transient electromagnetic detection; the directional long exploration adopts a secondary drilling structure and a screw motor directional drilling technology to realize accurate control of a deep stratum, the branch short exploration realizes dynamic and rapid verification through a TBM integrated drilling machine, meanwhile, in-hole transient electromagnetic exploration adopts three-component measurement and an intelligent inversion algorithm to fill the blank of geological information between drill holes, and by means of mutual data correction of the three methods, the detection accuracy is improved. A three-in-one detection system of drilling, geophysical prospecting and intelligent analysis is formed, and then accurate advanced prediction is achieved, so that the problems that a traditional single detection technology is low in precision and insufficient in coverage range are effectively solved, the water inrush risk is remarkably reduced, construction safety is guaranteed, and the method is effectively suitable for deep tunnel TBM safety construction.

The invention discloses a large-section long-footage mechanical blasting mixed excavation method suitable for a subway station, and relates to the technical field of underground rail transit engineering, and the method comprises the following steps: obtaining a tunnel section contour, a geologic structure and blasting parameters, building a three-dimensional blasting wave propagation inversion model, and combining geology and material heterogeneity to obtain a three-dimensional blasting wave propagation inversion model; and identifying a closed space region forming reflection interference, and predicting a space point location where blasting wave energy is abnormally accumulated. Based on the three-dimensional blasting wave propagation inversion model, the abnormal wave energy accumulation area is predicted in combination with geological heterogeneity, the high-risk blasting section is recognized, parameters are reconstructed, vibration real-time monitoring and model feedback correction are assisted, a closed-loop control system is constructed, local wave crest abnormity is effectively restrained, and the safety and controllability of large-section tunnel blasting construction are improved.

The invention discloses a real-time dynamic optimization adjustment method and system for a tunneling process, and relates to the technical field of intelligent tunneling, and the method comprises the following steps: obtaining the operation state data of tunneling equipment in real time, and recording the dynamic change characteristics of a stratum; the tunneling parameters are optimized and adjusted in real time, the tunneling machine is prevented from entering a resonance interval, fatigue damage of key components such as a cutter head and a thrust cylinder is reduced, the stability of equipment is improved, and the service life of the equipment is prolonged. A multi-objective optimization control strategy is adopted, and an intelligent algorithm is combined, so that the tunneling mode is automatically adjusted, the tunneling efficiency is improved, and manual intervention is reduced. According to the stratum prediction method based on machine learning, the adaptability of the heading machine to complex stratums is enhanced, stratum sudden change risks are warned in advance, and construction potential safety hazards are reduced. And meanwhile, an abnormal state emergency processing mechanism ensures that the tunneling process is stable and controllable, and the construction safety and reliability are improved.

The invention discloses a shield tunneling intelligent control method and system based on a ground-tunnel-machine-information-human adaptation level, and relates to the crossing field of underground engineering intelligent construction and information technology. Setting a first-level index and a second-level index; dividing the shield construction state into three levels of intelligent adaptation levels by combining the total score of the construction state with a dynamic threshold value; setting four targets of attitude intelligent control, settlement intelligent control, tunneling efficiency optimization and construction abnormity diagnosis; a machine learning prediction model is constructed, and the construction state parameters corresponding to the targets are predicted in real time; according to the shield intelligent adaptation level, the man-machine cooperation modes are divided into three classes of A level, B level and C level; and comparing actual data with a prediction result of the machine learning model, and dynamically correcting the control target parameters. Through real-time sensing of geological, mechanical and environmental multi-dimensional construction states and in combination with a dynamic weight distribution strategy, precise evaluation of the shield construction state is achieved, and meanwhile self-adaptive switching of the man-machine control right is achieved.

The invention discloses a heading equipment cutting head position calibration method based on image acquisition, which comprises the following steps: S1, acquiring image data and preprocessing the image data; s2, extracting cutting head feature points, and constructing a two-dimensional feature point set; s3, calculating a three-dimensional coordinate by using perspective transformation, and generating a point cloud set; s4, performing rigid registration on the point cloud set and a standard structure model of the tunneling equipment, and iteratively solving a rotation matrix and a translation vector to obtain pose parameters; s5, simulating change tracks of the feature points in continuous frames, constructing a time consistency constraint, and optimizing pose parameters; s6, the optimized three-dimensional coordinates are converted to a reference coordinate system, space alignment is completed, and a position calibration result is generated; and S7, outputting a calibration result for control instruction generation and path planning. According to the invention, high-precision and non-contact three-dimensional position calibration of the cutting head of the tunneling equipment is realized, and operation automation and control precision are improved.

A bilateral expanding excavation construction method for a middle pilot tunnel of a tunnel is provided. Section or excavation width of the middle pilot tunnel is generally not larger than that of a common double-line or double-lane tunnel; a step construction method is adopted, and the construction method is the same as that of a common double-line or double-lane tunnel; and the construction method is familiar to constructors, and the construction difficulty is avoided. A primary support section of the middle pilot tunnel can be expanded and excavated after being closed by at least 3 meters; if a side tunnel face is stable, and the problem of the operation height of manual installation of a steel frame and an anchor rod is solved, one-time excavation and support can be achieved, and the construction is more convenient; or otherwise, a side tunnel is excavated according to a two-step or three-step method. The primary support of the middle pilot tunnel can be timely closed; the primary support of the side tunnel and the primary support of the middle pilot tunnel can be completely connected in sequence, and various errors and deformation influences of surrounding rocks and the supportare left at an inverted arch part for treatment; and the construction progress is basically the same as that of the common double-line or double-lane tunnel.

The invention provides a box culvert jacking attitude intelligent control method and system based on digital twinning, and relates to the technical field of attitude control. Structural parameters of a box culvert entity, parameters of a construction environment, and elevation change data and axis offset data in a jacking process are obtained, and the two kinds of data are integrated to obtain a box culvert jacking attitude intelligent control system; generating a box culvert jacking attitude data frame sequence; according to box culvert entity reference information in the model and the updated box culvert real-time state information, combining the box culvert jacking attitude data frame sequence to determine box culvert actual attitude parameters, comparing the box culvert actual attitude parameters with box culvert design attitude parameters, determining attitude deviation types and attitude deviation values corresponding to the box culvert axis direction and the box culvert height direction respectively, and calculating the box culvert jacking attitude data frame sequence according to the attitude deviation types and the attitude deviation values. According to the method, the attitude adjustment control instruction is generated according to the attitude of the box culvert, so that the equipment drives the jack to adjust the jacking attitude of the box culvert according to the instruction, dynamic synchronization and accurate mapping of the structural state in the jacking process of the box culvert are realized, and the real-time performance, the accuracy and the control robustness of attitude adjustment are improved.

The invention discloses a device judging whether or not an earth pressure balance shield earth chamber is full. The device comprises a pressure detector disposed on the upper portion of a front shield earth chamber of the earth pressure balance shield; the signal output end of a pressure sensor is connected with an upper computer; the upper computer judges whether or not the chamber is full; if not, a warning signal is sent to an alarm unit. The device has the advantages that whether or not the earth chamber is full can be judged anytime during earth pressure balance shield tunneling, full-chamber shield tunneling can be ensured and the application of full-chamber tunneling decreases the danger of collapse ahead.

The invention relates to the technical field of tunnel engineering and intelligent construction, in particular to a shield tunneling real-time control method based on a random forest and a particle swarm optimization algorithm. The method comprises the steps that initial tunneling parameters are generated through a parameter recommendation random forest model according to geology and tunnel geometric parameters, and model hyper-parameters are optimized through a sparrow optimization algorithm; carrying out settlement prediction by utilizing the settlement prediction random forest model; if the predicted value exceeds the limit, carrying out iterative optimization by adopting a particle swarm optimization algorithm and taking the initial parameter as a starting point, and searching a global optimal tunneling parameter combination meeting the settlement requirement; finally, the optimized parameters are issued to the shield tunneling machine to be executed, the model is continuously updated based on real-time construction data, and closed-loop control is formed. According to the method, intelligent recommendation and real-time optimization of tunneling parameters can be realized, the ground surface settlement control precision and the system response speed are improved, the dependence on artificial experience is effectively reduced, and the self-adaptive capability and the intelligent level of shield construction under complex geological conditions are enhanced.

The invention provides a sandy stratum tunnel water burst risk prediction method. The method comprises the steps that a radar instantaneous energy spectrum in front of tunneling is obtained through a geological radar, a micro-seismic spectrum is obtained through a micro-seismic array, and a residual distance is obtained through laser ranging; aligning and coupling the two spectrums to obtain a water inrush risk index, inputting the water inrush risk index and the residual distance into a self-adaptive threshold comparator, and if the water inrush risk index and the residual distance exceed the limit, dynamically adjusting the rotating speed of a cutter head, propulsive force, the rotating speed of a screw conveyor and grouting pressure, and generating a risk avoidance path; and calling a water inrush risk model library constructed by LSTM-Attention, inputting a risk index sequence, a residual distance, cutterhead torque, a soil pressure gradient, a current change rate and other auxiliary variables, and outputting a future multi-step risk level. According to the method, accurate prediction and real-time avoidance of the water gushing risk of the sandy stratum tunnel can be realized, and the safety and efficiency of tunnel construction are improved.

The invention provides a tunnel hard rock non-blasting excavation method. The tunnel hard rock non-blasting excavation method can realize efficient excavation of complete or relatively complete hard rock without adopting large excavation equipment such as a heading machine and the like , and is characterized by comprising the following steps of 1, drilling contour holes along a tunnel face contour line so as to determine a tunnel excavation range, and determining each drilling and grooving position according to the excavation range, then, forming and drilling a plurality of cavity expanding holes in the position close to the positions; 2, drilling a plurality of rows of guide holes between the contour holes and the cavity expanding holes; 3, drilling groove forming holes which are meshed and overlapped with each other at the determined drilling and grooving positions, and finally forming a groove cavity; 4, enabling the cavity expanding holes near the groove cavity to be subjected to impact crushing, and therefore expanding excavation is conducted on the groove cavity; 5, according to a certain excavation sequence, starting from the guide holes close to the expanded and excavated groove cavity, adopting a hydraulic breaking hammer for conducting impact breaking on the guide holes, so that excavation of the rock mass of a main body part is completed; and 6, conducting impact crushing on the contour holes.

The invention discloses a sand gravel stratum pipe jacking force-stratum resistance real-time matching control method, belongs to the technical field of underground engineering pipe jacking construction, and solves the technical problems of pipe clamping, ground upheaval or settlement easily caused by mismatching of jacking force and stratum resistance in a sand gravel stratum. According to the method, the jacking force is calculated by collecting the pressure of a jacking oil cylinder in real time, the torque of a cutter head, the rotating speed of a spiral conveyor and the soil outlet pressure are synchronously obtained, and a resistance sudden change area is recognized and target resistance is dynamically predicted based on the sudden change gradient of the torque-rotating speed ratio; the jacking speed, the grouting pressure and the rotating speed of the spiral conveyor are adjusted in a linkage mode according to the real-time deviation of the jacking force and the target resistance; and through grouting closed-loop control and unearthed pressure fluctuation analysis, a penetration abnormal area is judged, a penetration coefficient is corrected, and real-time dynamic matching of jacking system parameters and formation resistance is achieved. The method is mainly used for safe and efficient propulsion control of sand gravel stratum pipe jacking construction.

The invention relates to the technical field of tunnel engineering automation and intelligent control, and discloses a TBM tunneling parameter intelligent optimization decision-making system based on an LSTM network, and the system comprises a data collection and preprocessing module which obtains external data and outputs a tunneling parameter sequence; the probabilistic tunneling trend prediction module is used for outputting a prediction expected value and prediction uncertainty; the prospective geological precursor sensing module is used for matching and identifying known risks and outputting alarm events; and determining an optimal tunneling mode by the dynamic risk avoidance decision matrix. When the prediction uncertainty is too high, activating the prospective template driven by the uncertainty to excavate a new precursor template and update the template library; meanwhile, the decision-efficiency relevance evaluation and strategy self-optimization engine optimizes the decision rule according to the actual tunneling efficiency. According to the method, decision making is carried out through quantitative risk prediction and fusion of multi-source information, and a double learning closed loop of knowledge discovery and strategy optimization is established, so that the reliability, the adaptability and the long-term efficiency of system decision making are remarkably improved.

The invention discloses a shield tunnel muck improvement parameter prediction method and system, and relates to the technical field of tunnel engineering construction.The method comprises the steps that a multi-modal sensor is arranged at a key part of a shield tunneling machine, and tunneling parameters, stratum parameters, multi-modal sensor signals, muck physical properties and modifier injection parameters are collected; then, extracting change characteristics, calculating mutation sensitive factors and carrying out working condition judgment; constructing a normal prediction sub-model library and a sudden change quick response model, calculating a stratum model adaptation index and performing model suitability judgment; establishing a working condition coupling prediction model by using a graph neural network, extracting nonlinear coupling characteristics, calculating a working condition coupling index and judging prediction stability; actual construction performance data are collected and compared with a model prediction result, a prediction correction index is calculated, deviation analysis is carried out, and a corresponding correction strategy is triggered. According to the method, intelligent prediction and dynamic correction of shield tunnel muck improvement parameters can be achieved, and the safety and stability of the construction process are improved.

The invention relates to the technical field of tunnel construction, and discloses a coal mine goaf karst tunnel construction method which comprises the following steps: S1, performing three-dimensional scanning on the axis and the periphery of a tunnel by adopting a high-frequency geological radar in combination with aerial survey of an unmanned aerial vehicle, and accurately positioning the boundary of a goaf, the development form of a karst cave and filler distribution; and S2, integrating a seismic wave reflection method, drilling and coring data and a geological sketch result, constructing a three-dimensional geological model, and dynamically predicting a potential water inrush and mud inrush risk area. According to the method, a high-frequency geological radar is combined with aerial survey of an unmanned aerial vehicle, three-dimensional scanning is conducted on the axis and the periphery of a tunnel, geological radar data, a point cloud model and tunnel design BIM are integrated, the boundary of a goaf (the error is smaller than or equal to 0.3 m) and the karst cave form are marked, and the collapse risk level is predicted based on rock mass mechanical parameters (compressive strength and fracture density) and hydrological data (seepage paths). Therefore, the method has the advantages of advanced detection, multi-source data fusion and the like, and the resolution of geological forecast is improved.

The invention discloses a shield muck coarse particle gradation real-time identification system and method based on multi-modal machine vision, and relates to the field of muck particle identification, and the system comprises a multi-modal data collection module which is composed of a plurality of groups of sensors and is used for collecting multi-modal data of muck particles; the edge calculation processing unit is used for preprocessing the multi-modal data; the analysis module is used for constructing a deep learning model and realizing grading prediction and mud content prediction; and the control module is used for real-time feedback and control including visualization, decision support and tunneling parameter adaptive adjustment. According to the system, accurate real-time analysis of geometric, image and material features of muck particles is realized through multi-modal sensor fusion and edge calculation efficient preprocessing in combination with feature-level / decision-level fusion and a lightweight deep learning model; by means of closed-loop control, the method has high adaptability and engineering intelligent feedback capacity, and muck grading recognition precision and tunneling parameter self-adaptive adjustment efficiency in shield construction are remarkably improved.

The invention discloses a tunnel boring machine (TBM) tunneling method for a large-diameter inclined shaft under a deep coal mine well, belongs to the technical field of mine construction engineering and tunnel tunneling, and aims to solve the problems of poor axis control precision and deviation correction response lag caused by the fact that an existing method depends on manpower and cannot integrate geology and inclination angle information for self-adaptive thrust distribution. The surrounding rock strength grade is judged according to the torque and the speed by collecting the cutterhead torque, the rotating speed, the total propulsive force and the pitch angle in real time; calculating a gravity component force and an unstable moment by combining the pitch angle, and calculating a required deviation correction moment according to the real-time angle deviation; a pre-stored data table is inquired based on the pitch angle, the surrounding rock grade and the deviation rectifying torque, and the target pressure distribution proportion of the upper and lower partition oil cylinders is dynamically generated; and finally, the total propulsive force set value is decomposed according to the proportion, the pressure of an oil cylinder is adjusted through independent closed-loop control, and self-adaptive accurate control over the pitch attitude is achieved. The method is mainly used for improving the axis control precision and stability of the large-diameter inclined shaft TBM construction of the deep coal mine.

The invention discloses a comprehensive intelligent monitoring decision-making system and method for an overlapping shield tunnel system. The system comprises an information acquisition system, an iterative optimization state real-time evaluation system and an intelligent maintenance decision-making system. Firstly, a preset model is trained through data such as numerical calculation; operating measured data to train a feedback model, and weighting output results of the two models to obtain a tunnel deformation predicted value; then establishing an experience overlapping tunnel safety evaluation system and an iterative optimization evaluation system, performing deep association on the tunnel state, damage and maintenance decision through association degree intelligent real-time analysis, realizing tunnel state real-time monitoring, tunnel damage real-time capturing and tunnel maintenance decision real-time updating, performing intelligent matching on maintenance measures, and performing real-time monitoring on the tunnel state, the tunnel damage and the maintenance decision. And the prediction model and the safety evaluation system are iteratively optimized according to operation effect feedback. According to the invention, real-time perception, intelligent diagnosis and maintenance of the whole life cycle overall service performance of the overlapping shield system are realized, and the long-term service quality of the overlapping shield tunnel structure is improved.

The invention discloses a combined well spacing method for a ground vertical well and an underground horizontal well, which comprises the following steps of: determining estimated fracturing information of the vertical well and estimated fracturing information of the horizontal well according to acquired fracturing construction information of a surrounding mine; the width of the to-be-mined working face and the lateral mining-induced fracture range of the mined working face are obtained, vertical well construction information is determined according to the width of the to-be-mined working face, the lateral mining-induced fracture range and vertical well estimated fracturing information, fracturing construction is conducted on the thick and hard roof based on the vertical well construction information, and the vertical well fracturing range of a ground vertical well is obtained; and horizontal well construction information is determined according to the horizontal well estimated fracturing information and the vertical well fracturing range, and fracturing construction is conducted on the impact main control rock stratum based on the horizontal well construction information. According to the combined well spacing method for the ground vertical well and the underground horizontal well, the well spacing scheme that the ground vertical well and the underground horizontal well are matched with each other is adopted, sufficient fracturing and pressure relief can be conducted on a thick and hard top plate, and cooperative prevention and control over rock burst and mine earthquake disasters are achieved.

The invention discloses a layered settlement prediction method and device based on multi-source data fusion and a medium, and relates to the technical field of shield tunnel construction monitoring. The method comprises the following steps: acquiring multi-source monitoring data, and preprocessing the multi-source monitoring data to obtain standard monitoring data; wherein the multi-source monitoring data comprises layered settlement monitoring data, shield construction parameters and geological survey data; inputting the standard monitoring data into a preset layered settlement prediction model to obtain a layered settlement prediction result; wherein the layered settlement prediction result comprises the layered settlement amount and the settlement rate in the unit distance before and after the shield tunneling machine in the unit time in the future; and based on a layered settlement prediction result, triggering graded early warning, and generating a visual decision report. By constructing the layered settlement prediction model, the coupling relationship among the shield construction parameters, the address survey data and the settlement of different depths is excavated, and the settlement of the shield tunneling machine in the unit time in the future and the specific distance before and after the shield tunneling machine is accurately predicted.

The invention relates to a deep stratum well hole and cave mining system and method, the system comprises a traffic well hole system, the traffic well hole system is provided with at least one well hole smaller than 1 m, an internal channel formed by the well hole is communicated with a cave formed by excavation, and the internal channel formed by the well hole is configured to be a flow-back channel and / or an injection channel; the well hole and cave mining device is provided with a crushing assembly and / or a cleaning and transporting assembly, the crushing assembly is used for carrying out form-controllable mining on the ore on the cave rock wall surface, and the cleaning and transporting assembly is used for cleaning ore particles in the cave or transporting the ore particles to the flowback channel; and the particle flow lifting system is used for lifting the extracted ore particles out of the well through the flow-back channel. The technical problems that when caves of deep stratums are mined, the supporting effect is poor, and the ore particle clearing and transporting efficiency is low are solved, and large-scale and high-efficiency mining operation can be conducted on the deep stratums or non-hard stratums.

The invention belongs to the field of shield tunneling machine deviation rectification track planning, and discloses a shield tunneling machine deviation rectification track high-precision autonomous planning method and system, and the method comprises the steps: determining the minimum turning radius of a shield tunneling machine; carrying out coordinate calculation on the DTA to obtain the coordinate, the direction and the curvature of each point; projecting the starting point state and the end point state of the shield tunneling machine under the Cartesian coordinate system into the established Frenet coordinate system, establishing a correction curve parameterization equation based on a quintic polynomial line type, and converting the state of the shield tunneling machine into the Cartesian coordinate system; through segment pre-typesetting constraint, determining a deviation correction curve end point feasible region; the deviation degree of the deviation correction curve and the DTA and the length and smoothness of the deviation correction curve serve as optimization parameters, normalization processing is carried out according to the threshold value of each target, a comprehensive cost function is established, an improved particle swarm optimization algorithm is used for solving, and a global optimal deviation correction track is output. According to the method, real-time autonomous high-precision deviation rectification track planning of the shield tunneling machine can be realized, the track feasibility is improved, the pose deviation degree is reduced, and the tunnel quality and the construction efficiency are improved.

The invention relates to the technical field, in particular to a method for determining advance grouting reinforcement parameters of a tunnel fault fracture zone. The grouting pressure, the rate, the water cement ratio, the diffusion radius, the gelation time and the grouting amount per unit length are comprehensively covered through parameter grid structure division, the consistency of numerical values of all nodes is guaranteed through rock core strength and anchor rod tension data checking, and a combination with the error exceeding a threshold value generates a new solution through variation and crossover operation in a differential evolution algorithm. The global search capability is improved, combinations which do not conform to conditions are quickly eliminated, the global search capability can be enhanced, a low-quality solution can be quickly replaced, pheromone concentration is directly generated based on combination fitness, the path selection probability is calculated according to a fitness ratio, and a high-quality path is adaptively enhanced by utilizing an ant colony algorithm. The convergence speed is improved, the risk of falling into local optimum is reduced, iteration strengthening and dynamic search direction adjustment of a high-quality solution are achieved, and the convergence speed and result stability of the optimal solution are improved.

The invention belongs to the technical field of pipe jacking machines, and discloses a high-strength rock crushing pipe jacking machine which comprises a machine head and a machine tail, the machine head is rotationally connected with a mounting frame with a cutter head, a partition plate in the machine head is rotationally connected with a shaft sleeve, and the shaft sleeve is rotationally connected with a driving shaft connected with the cutter head. A motor on the partition plate is meshed with a driving shaft through a driving bevel gear and a driven bevel gear II and a driven bevel gear I on a shaft sleeve, so that the driving shaft and the driven bevel gear II and the driven bevel gear I respectively drive a crushing roller II and a crushing roller I to act. The shaft sleeves drive the baffles to slide along the sliding grooves through the tooth grooves, the gears and the connecting rods to be matched with the transfer openings of the partition plates and the diversion groove bodies. An auger conveying pipe connected with the flow guide groove body penetrates through the mounting plates of the machine head and the machine tail and is supported by the machine tail support, and a collecting box on the machine tail base is matched with the auger conveying pipe. According to the pipe jacking machine, cutter head cutting and double crushing roller secondary crushing are combined with baffle control to prolong the crushing time, the rock crushing effect is improved, conveying blockage is avoided, and the pipe jacking machine is suitable for high-strength rock stratum construction.

The invention discloses a shield construction muck quantity real-time monitoring system and method based on three-dimensional laser scanning, and relates to the technical field of construction monitoring, the shield construction muck quantity real-time monitoring system comprises a hardware module, a software algorithm module and a system integration module, the software algorithm module comprises a point cloud data processing module, a volume calculation module, a data analysis module and an early warning module, the point cloud data processing module adopts a hierarchical noise filtering algorithm and performs point cloud completion based on a generative adversarial network, and the volume calculation module adopts a slice integration method. Through a hierarchical noise filtering algorithm and a generative adversarial network point cloud complementation technology, noise points generated by interference of dust, diffuse reflection and the like are effectively eliminated, sparse point clouds and large-area holes in a low-reflectivity area are repaired, a complete and high-precision muck pile three-dimensional surface model is reconstructed, a reliable data basis is provided for volume calculation, and the method is suitable for mass production. And the point cloud quality and the model integrity in a complex construction environment are remarkably improved.

The invention relates to the technical field of tunnel and underground engineering construction, and discloses an intelligent closed-loop control system and method for a tunnel construction section contour, and the method comprises the steps: collecting geological-geometric data through the fusion of a three-dimensional laser scanner and a geological radar before blasting; dynamically generating a theoretical blasting contour line and adaptive blasting parameters through a convolutional neural network; iMU positioning and a PID controller are combined to realize millimeter-level accurate control of the drill boom; generating a three-dimensional deviation field by adopting an iterative point cloud registration algorithm after blasting; when the deviation exceeds a threshold value, triggering incremental learning to optimize model parameters; and finally, closed-loop iteration is formed. Through multi-source data fusion, contour and parameter intelligent generation, precise execution control, three-dimensional deviation field feedback and an incremental learning mechanism, a complete intelligent closed-loop control system is constructed, the limitation of traditional blasting design depending on experience is broken through, and the precision, efficiency and economic benefits of tunnel construction are remarkably improved.

The present disclosure provides a tunnel boring machine excavation parameter prediction method based on offline reinforcement learning and a device. The method comprises: acquiring historical operating data generated by a tunnel boring machine within a specified time interval under normal operating conditions, and preprocessing the data; constructing a tunnel boring machine excavation parameter prediction model on the basis of an offline reinforcement learning model, and training the tunnel boring machine excavation parameter prediction model by means of historical point data in the preprocessed historical operating data; and inputting point data generated by the tunnel boring machine during real-time operation into the trained tunnel boring machine excavation parameter prediction model, and outputting a predicted value of a tunnel boring machine excavation parameter to guide the excavation of the tunnel boring machine. By means of the present invention, a corresponding excavation parameter can be predicted on the basis of point data generated by a tunnel boring machine during operation, providing guidance for a tunnel boring machine operator.

The utility model discloses a screw conveyor screw shaft blade wear detection device, and solves the problems of high difficulty and low precision of screw shaft blade wear detection in the prior art. The utility model relates to a screw conveyor screw shaft blade wear detection device, which comprises a fixed seat arranged on a screw conveyor cylinder in a sealing manner, a telescopic driving piece with a stroke sensor is arranged on the fixed seat, and one end of the telescopic driving piece extending into the screw conveyor cylinder is connected with a detection plate corresponding to a screw blade. The detection plate is provided with an abrasion detection piece for measuring the abrasion loss of the detection plate; according to the device, the oil cylinder, the displacement sensor and the like are adopted, and the function of rapidly measuring the abrasion loss of the spiral shaft can be achieved through the displacement generated when the oil cylinder pushes the lower detection plate in a closed environment. The wear detection device provided by the utility model solves the problems of difficulty in wear detection, high detection cost, untimely detection, low detection precision and the like of the screw shaft of the screw conveyer on site, and realizes the purposes of convenience, rapidness and high accuracy in wear detection of the screw shaft.

The invention belongs to the technical field of tunnel pipe jacking construction offset detection, and particularly relates to a tunnel pipe jacking construction offset measurement and analysis system and method. Cross validation and detection calibration are carried out by using dynamic synchronism of stress change and attitude response in time sequence, a consistency criterion of multi-source sensing data is constructed, a data distortion phenomenon can be identified in time under an abnormal working condition, the reliability and fault-tolerant capability of an offset monitoring result are improved, a high-credibility state sensing basis is provided for a construction process, and the construction period is shortened. Based on the calibrated stress distribution data and attitude measurement data, a stress concentration main direction and an attitude offset direction are extracted, a space correlation model between the stress concentration main direction and the attitude offset direction is established, and then the model is combined with real-time stress information of the current tunneling position to predict the attitude offset trend of the pipe jacking machine. And the prospective judgment on the change of the tunneling posture can be realized.